The need for large-scale numerical simulation is currently driving the
development of the largest and most powerful parallel computers in the
world. Activities at U.S. government laboratories, for example, are
currently replacing underground nuclear testing with computational
predictive simulation. A major challenge is to design software and
algorithms that can efficiently utilize upwards of ten thousand
parallel processors.
This talk addresses the problem of solving large, sparse linear
(matrix) equations on these large parallel computers. These equations
arise in all implicit methods for numerical simulation and usually form
the bottleneck in the computation. We will introduce how these
equations arise, and describe a novel method of approximating the
inverse of a matrix by a sparse matrix. This is an elegant and
inherently parallel method used to "precondition" the original set of
equations in order to make them easier to solve by a parallel,
iterative procedure. For perspective, we will intuitively describe
other state-of-the-art methods, as well as computer science issues for
achieving high computing performance.